 *-* LIMDEP *-* File created 02/25/95 / 18:07:36


       Reading file : BERRY.DAT
Current sample      :      576 observs.
Total variables now :  16
Missing values read :                 0
  now run probits on full data set -- models not including instit2 

                                                                         
 MODEL COMMAND:
 PROBIT;LHS=ADOPNEW;RHS=ONE,INCMDF_1,URBINT_1,FISCAL_1,ELECT1
 ,ELECT2,AD1_20,IDEOL1$                            
Dependent variable is binary, y=0 or y not equal 0
Ordinary    least squares regression.     Dep. Variable     =  ADOPNEW
Observations       =            576       Weights           =  ONE
Mean of LHS        =  0.4861111E-01       Std.Dev of LHS    =  0.2152406E+00
StdDev of residuals=  0.2132636E+00       Sum of squares    =  0.2583340E+02
R-squared          =  0.3023722E-01       Adjusted R-squared=  0.1828592E-01
F[  7,   568]      =  0.2530036E+01       Prob value           0.1442487E-01
Log-likelihood     =  0.7676992E+02       Restr.(�=0) Log-l =  0.6792723E+02
Amemiya Pr. Criter.=  0.4611303E-01       Akaike Info.Crit. = -0.2387844E+00
ANOVA  Source         Variation     Degrees of Freedom       Mean Square
       Regression     0.8054860E+00            7.              0.1150694E+00
       Residual       0.2583340E+02          568.              0.4548134E-01
       Total          0.2663889E+02          575.              0.4632850E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  0.74382E-02   0.5972E-01    0.125  0.90088
INCMDF_1  0.92763E-03   0.5834E-03    1.590  0.11184   73.488        16.498
URBINT_1 -0.18577E-03   0.6215E-03   -0.299  0.76502   64.768        14.483
FISCAL_1 -0.19733       0.9564E-01   -2.063  0.03910 -0.16668       0.98534E-01
ELECT1   -0.81063E-01   0.2247E-01   -3.608  0.00031  0.32465       0.46865
ELECT2   -0.41901E-01   0.2183E-01   -1.919  0.05494  0.35069       0.47760
AD1_20    0.93400E-02   0.1357E-01    0.689  0.49113  0.29861       0.66547
IDEOL1   -0.32318E-01   0.2622E-01   -1.232  0.21781  0.29807       0.36330

*******************************************************************************
** Gradient has converged.
** Function has converged.
** B-vector has converged.

*******************************************************************************


Binomial Probit Model
Maximum Likelihood Estimates
Log-Likelihood..............   -101.7255
Restricted (Slopes=0) Log-L.   -111.9774
Chi-Squared ( 7)............    20.50378
Significance Level..........   0.4578393E-02
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  -2.3326       0.6351       -3.673  0.00024
INCMDF_1  0.10608E-01   0.6084E-02    1.743  0.08126   73.488        16.498
URBINT_1 -0.19248E-02   0.6486E-02   -0.297  0.76665   64.768        14.483
FISCAL_1  -2.3385        1.055       -2.217  0.02665 -0.16668       0.98534E-01
ELECT1    -1.1108       0.3333       -3.333  0.00086  0.32465       0.46865
ELECT2   -0.36149       0.2058       -1.756  0.07906  0.35069       0.47760
AD1_20    0.13109       0.1439        0.911  0.36227  0.29861       0.66547
IDEOL1   -0.36556       0.2893       -1.264  0.20630  0.29807       0.36330

Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.

            Predicted

Actual       0     1       TOTAL

  0        548     0         548
  1         28     0          28

TOTAL      576     0         576















                                                                         
 MODEL COMMAND:
 PROBIT;LHS=ADOPNEW;RHS=ONE,INCMDF_1,URBINT_1,FISCAL_1,ELECT1
 ,ELECT2,AD1_20,INSTCOPE$                            
Dependent variable is binary, y=0 or y not equal 0
Ordinary    least squares regression.     Dep. Variable     =  ADOPNEW
Observations       =            576       Weights           =  ONE
Mean of LHS        =  0.4861111E-01       Std.Dev of LHS    =  0.2152406E+00
StdDev of residuals=  0.2134844E+00       Sum of squares    =  0.2588694E+02
R-squared          =  0.2822734E-01       Adjusted R-squared=  0.1625127E-01
F[  7,   568]      =  0.2356978E+01       Prob value           0.2220801E-01
Log-likelihood     =  0.7617364E+02       Restr.(�=0) Log-l =  0.6792723E+02
Amemiya Pr. Criter.=  0.4620860E-01       Akaike Info.Crit. = -0.2367140E+00
ANOVA  Source         Variation     Degrees of Freedom       Mean Square
       Regression     0.7519450E+00            7.              0.1074207E+00
       Residual       0.2588694E+02          568.              0.4557561E-01
       Total          0.2663889E+02          575.              0.4632850E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  0.20081E-01   0.5895E-01    0.341  0.73337
INCMDF_1  0.79089E-03   0.5745E-03    1.377  0.16864   73.488        16.498
URBINT_1 -0.19908E-03   0.6221E-03   -0.320  0.74896   64.768        14.483
FISCAL_1 -0.18553       0.9700E-01   -1.913  0.05578 -0.16668       0.98534E-01
ELECT1   -0.81195E-01   0.2250E-01   -3.609  0.00031  0.32465       0.46865
ELECT2   -0.40012E-01   0.2183E-01   -1.833  0.06676  0.35069       0.47760
AD1_20    0.98848E-02   0.1360E-01    0.727  0.46743  0.29861       0.66547
INSTCOPE -0.20852E-03   0.3572E-03   -0.584  0.55934   48.828        26.679

*******************************************************************************
** Gradient has converged.
** Function has converged.
** B-vector has converged.

*******************************************************************************


Binomial Probit Model
Maximum Likelihood Estimates
Log-Likelihood..............   -102.3540
Restricted (Slopes=0) Log-L.   -111.9774
Chi-Squared ( 7)............    19.24682
Significance Level..........   0.7448315E-02
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  -2.1625       0.6247       -3.462  0.00054
INCMDF_1  0.87425E-02   0.5843E-02    1.496  0.13456   73.488        16.498
URBINT_1 -0.18960E-02   0.6470E-02   -0.293  0.76948   64.768        14.483
FISCAL_1  -2.1710        1.050       -2.068  0.03864 -0.16668       0.98534E-01
ELECT1    -1.1124       0.3325       -3.346  0.00082  0.32465       0.46865
ELECT2   -0.34710       0.2050       -1.693  0.09040  0.35069       0.47760
AD1_20    0.14542       0.1436        1.013  0.31117  0.29861       0.66547
INSTCOPE -0.23362E-02   0.3668E-02   -0.637  0.52418   48.828        26.679

Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.

            Predicted

Actual       0     1       TOTAL

  0        548     0         548
  1         28     0          28

TOTAL      576     0         576















                                                                         
 MODEL COMMAND:
 PROBIT;LHS=ADOPNEW;RHS=ONE,INCMDF_1,URBINT_1,FISCAL_1,ELECT1
 ,ELECT2,AD1_20,INSTALL$                            
Dependent variable is binary, y=0 or y not equal 0
Ordinary    least squares regression.     Dep. Variable     =  ADOPNEW
Observations       =            576       Weights           =  ONE
Mean of LHS        =  0.4861111E-01       Std.Dev of LHS    =  0.2152406E+00
StdDev of residuals=  0.2134795E+00       Sum of squares    =  0.2588574E+02
R-squared          =  0.2827251E-01       Adjusted R-squared=  0.1629700E-01
F[  7,   568]      =  0.2360860E+01       Prob value           0.2199572E-01
Log-likelihood     =  0.7618703E+02       Restr.(�=0) Log-l =  0.6792723E+02
Amemiya Pr. Criter.=  0.4620645E-01       Akaike Info.Crit. = -0.2367605E+00
ANOVA  Source         Variation     Degrees of Freedom       Mean Square
       Regression     0.7531484E+00            7.              0.1075926E+00
       Residual       0.2588574E+02          568.              0.4557349E-01
       Total          0.2663889E+02          575.              0.4632850E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  0.19214E-01   0.5895E-01    0.326  0.74447
INCMDF_1  0.78647E-03   0.5714E-03    1.376  0.16869   73.488        16.498
URBINT_1 -0.19134E-03   0.6225E-03   -0.307  0.75858   64.768        14.483
FISCAL_1 -0.19077       0.9943E-01   -1.919  0.05502 -0.16668       0.98534E-01
ELECT1   -0.81109E-01   0.2249E-01   -3.606  0.00031  0.32465       0.46865
ELECT2   -0.40209E-01   0.2182E-01   -1.843  0.06535  0.35069       0.47760
AD1_20    0.98249E-02   0.1360E-01    0.723  0.46990  0.29861       0.66547
INSTALL  -0.22788E-03   0.3760E-03   -0.606  0.54450   45.221        25.776

*******************************************************************************
** Gradient has converged.
** Function has converged.
** B-vector has converged.

*******************************************************************************


Binomial Probit Model
Maximum Likelihood Estimates
Log-Likelihood..............   -102.3395
Restricted (Slopes=0) Log-L.   -111.9774
Chi-Squared ( 7)............    19.27590
Significance Level..........   0.7365599E-02
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  -2.1694       0.6241       -3.476  0.00051
INCMDF_1  0.86687E-02   0.5805E-02    1.493  0.13536   73.488        16.498
URBINT_1 -0.18307E-02   0.6469E-02   -0.283  0.77717   64.768        14.483
FISCAL_1  -2.2267        1.069       -2.083  0.03721 -0.16668       0.98534E-01
ELECT1    -1.1125       0.3326       -3.345  0.00082  0.32465       0.46865
ELECT2   -0.34972       0.2050       -1.706  0.08804  0.35069       0.47760
AD1_20    0.14472       0.1434        1.009  0.31292  0.29861       0.66547
INSTALL  -0.25299E-02   0.3841E-02   -0.659  0.51007   45.221        25.776

Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.

            Predicted

Actual       0     1       TOTAL

  0        548     0         548
  1         28     0          28

TOTAL      576     0         576














 Rejecting if -> NEW;INSTIT2=-999
Sample set to -> ALL                                                           
Rows 1 to   576 contain valid data.                                            

                                                                         
 MODEL COMMAND:
 PROBIT;LHS=ADOPNEW;RHS=ONE,INCMDF_1,URBINT_1,FISCAL_1,ELECT1
 ,ELECT2,AD1_20,IDEOL1,INSTIDEO$                             
Dependent variable is binary, y=0 or y not equal 0
Ordinary    least squares regression.     Dep. Variable     =  ADOPNEW
Observations       =            552       Weights           =  ONE
Mean of LHS        =  0.4710145E-01       Std.Dev of LHS    =  0.2120480E+00
StdDev of residuals=  0.2100927E+00       Sum of squares    =  0.2396744E+02
R-squared          =  0.3260973E-01       Adjusted R-squared=  0.1835720E-01
F[  8,   543]      =  0.2287996E+01       Prob value           0.2049201E-01
Log-likelihood     =  0.8251697E+02       Restr.(�=0) Log-l =  0.7336666E+02
Amemiya Pr. Criter.=  0.4485860E-01       Akaike Info.Crit. = -0.2663658E+00
ANOVA  Source         Variation     Degrees of Freedom       Mean Square
       Regression     0.8079179E+00            8.              0.1009897E+00
       Residual       0.2396744E+02          543.              0.4413894E-01
       Total          0.2477536E+02          551.              0.4496436E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  0.65188E-02   0.5934E-01    0.110  0.91253
INCMDF_1  0.84850E-03   0.5886E-03    1.442  0.14944   73.048        16.510
URBINT_1 -0.30403E-03   0.6236E-03   -0.488  0.62585   64.382        14.574
FISCAL_1 -0.24201       0.9717E-01   -2.491  0.01275 -0.16811       0.96669E-01
ELECT1   -0.75128E-01   0.2271E-01   -3.308  0.00094  0.32246       0.46784
ELECT2   -0.34849E-01   0.2195E-01   -1.587  0.11241  0.35507       0.47897
AD1_20    0.11380E-01   0.1348E-01    0.844  0.39849  0.30797       0.67580
IDEOL1   -0.51149E-01   0.3751E-01   -1.364  0.17273  0.28567       0.35136
INSTIDEO  0.49473E-01   0.4123E-01    1.200  0.23017  0.10507       0.30692

*******************************************************************************
** Gradient has converged.
** Function has converged.
** B-vector has converged.

*******************************************************************************


Binomial Probit Model
Maximum Likelihood Estimates
Log-Likelihood..............   -94.11071
Restricted (Slopes=0) Log-L.   -104.8196
Chi-Squared ( 8)............    21.41772
Significance Level..........   0.6116794E-02
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  -2.4031       0.6525       -3.683  0.00023
INCMDF_1  0.10517E-01   0.6429E-02    1.636  0.10183   73.048        16.510
URBINT_1 -0.35405E-02   0.6684E-02   -0.530  0.59630   64.382        14.574
FISCAL_1  -3.0863        1.143       -2.699  0.00695 -0.16811       0.96669E-01
ELECT1    -1.0761       0.3423       -3.144  0.00167  0.32246       0.46784
ELECT2   -0.31742       0.2136       -1.486  0.13718  0.35507       0.47897
AD1_20    0.14816       0.1471        1.007  0.31395  0.30797       0.67580
IDEOL1   -0.64910       0.4616       -1.406  0.15968  0.28567       0.35136
INSTIDEO  0.57484       0.4681        1.228  0.21947  0.10507       0.30692

Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.

            Predicted

Actual       0     1       TOTAL

  0        526     0         526
  1         26     0          26

TOTAL      552     0         552















                                                                         
 MODEL COMMAND:
 PROBIT;LHS=ADOPNEW;RHS=ONE,INCMDF_1,URBINT_1,FISCAL_1,ELECT1
 ,ELECT2,AD1_20,INSTCOPE,COPINSID$                           
   
Dependent variable is binary, y=0 or y not equal 0
Ordinary    least squares regression.     Dep. Variable     =  ADOPNEW
Observations       =            552       Weights           =  ONE
Mean of LHS        =  0.4710145E-01       Std.Dev of LHS    =  0.2120480E+00
StdDev of residuals=  0.2104464E+00       Sum of squares    =  0.2404821E+02
R-squared          =  0.2934992E-01       Adjusted R-squared=  0.1504936E-01
F[  8,   543]      =  0.2052362E+01       Prob value           0.3856615E-01
Log-likelihood     =  0.8158849E+02       Restr.(�=0) Log-l =  0.7336666E+02
Amemiya Pr. Criter.=  0.4500976E-01       Akaike Info.Crit. = -0.2630018E+00
ANOVA  Source         Variation     Degrees of Freedom       Mean Square
       Regression     0.7271548E+00            8.              0.9089435E-01
       Residual       0.2404821E+02          543.              0.4428767E-01
       Total          0.2477536E+02          551.              0.4496436E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  0.87632E-02   0.6052E-01    0.145  0.88486
INCMDF_1  0.69128E-03   0.5846E-03    1.182  0.23702   73.048        16.510
URBINT_1 -0.23705E-03   0.6244E-03   -0.380  0.70422   64.382        14.574
FISCAL_1 -0.24052       0.9904E-01   -2.428  0.01516 -0.16811       0.96669E-01
ELECT1   -0.75796E-01   0.2275E-01   -3.332  0.00086  0.32246       0.46784
ELECT2   -0.34234E-01   0.2200E-01   -1.556  0.11965  0.35507       0.47897
AD1_20    0.11407E-01   0.1354E-01    0.842  0.39958  0.30797       0.67580
INSTCOPE -0.45265E-04   0.4580E-03   -0.099  0.92126   49.139        26.775
COPINSID -0.10382E-03   0.4334E-03   -0.240  0.81066   19.489        27.991

*******************************************************************************
** Gradient has converged.
** Function has converged.
** B-vector has converged.

*******************************************************************************


Binomial Probit Model
Maximum Likelihood Estimates
Log-Likelihood..............   -95.10881
Restricted (Slopes=0) Log-L.   -104.8196
Chi-Squared ( 8)............    19.42152
Significance Level..........   0.1276083E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  -2.3509       0.6692       -3.513  0.00044
INCMDF_1  0.82898E-02   0.6165E-02    1.345  0.17871   73.048        16.510
URBINT_1 -0.23898E-02   0.6632E-02   -0.360  0.71857   64.382        14.574
FISCAL_1  -2.9425        1.143       -2.575  0.01002 -0.16811       0.96669E-01
ELECT1    -1.0890       0.3428       -3.177  0.00149  0.32246       0.46784
ELECT2   -0.31572       0.2119       -1.490  0.13616  0.35507       0.47897
AD1_20    0.15716       0.1471        1.069  0.28525  0.30797       0.67580
INSTCOPE -0.43194E-03   0.4709E-02   -0.092  0.92691   49.139        26.775
COPINSID -0.12456E-02   0.4474E-02   -0.278  0.78072   19.489        27.991

Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.

            Predicted

Actual       0     1       TOTAL

  0        526     0         526
  1         26     0          26

TOTAL      552     0         552















                                                                         
 MODEL COMMAND:
 PROBIT;LHS=ADOPNEW;RHS=ONE,INCMDF_1,URBINT_1,FISCAL_1,ELECT1
 ,ELECT2,AD1_20,INSTALL,ALLINSID$                            
  
Dependent variable is binary, y=0 or y not equal 0
Ordinary    least squares regression.     Dep. Variable     =  ADOPNEW
Observations       =            552       Weights           =  ONE
Mean of LHS        =  0.4710145E-01       Std.Dev of LHS    =  0.2120480E+00
StdDev of residuals=  0.2104323E+00       Sum of squares    =  0.2404500E+02
R-squared          =  0.2947930E-01       Adjusted R-squared=  0.1518065E-01
F[  8,   543]      =  0.2061684E+01       Prob value           0.3762824E-01
Log-likelihood     =  0.8162528E+02       Restr.(�=0) Log-l =  0.7336666E+02
Amemiya Pr. Criter.=  0.4500376E-01       Akaike Info.Crit. = -0.2631351E+00
ANOVA  Source         Variation     Degrees of Freedom       Mean Square
       Regression     0.7303602E+00            8.              0.9129503E-01
       Residual       0.2404500E+02          543.              0.4428177E-01
       Total          0.2477536E+02          551.              0.4496436E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  0.81273E-02   0.6033E-01    0.135  0.89284
INCMDF_1  0.70032E-03   0.5803E-03    1.207  0.22750   73.048        16.510
URBINT_1 -0.23146E-03   0.6244E-03   -0.371  0.71089   64.382        14.574
FISCAL_1 -0.24478       0.1012       -2.419  0.01557 -0.16811       0.96669E-01
ELECT1   -0.75803E-01   0.2274E-01   -3.333  0.00086  0.32246       0.46784
ELECT2   -0.34308E-01   0.2199E-01   -1.560  0.11871  0.35507       0.47897
AD1_20    0.11430E-01   0.1353E-01    0.845  0.39830  0.30797       0.67580
INSTALL  -0.67521E-04   0.4953E-03   -0.136  0.89157   45.463        25.851
ALLINSID -0.12226E-03   0.4670E-03   -0.262  0.79347   18.455        26.743

*******************************************************************************
** Gradient has converged.
** Function has converged.
** B-vector has converged.

*******************************************************************************


Binomial Probit Model
Maximum Likelihood Estimates
Log-Likelihood..............   -95.07000
Restricted (Slopes=0) Log-L.   -104.8196
Chi-Squared ( 8)............    19.49915
Significance Level..........   0.1240646E-01
N[0,1] used for significance levels.
Variable  Coefficient  Std. Error   t-ratio Prob|t|�x   Mean of X  Std.Dev.of X
-------------------------------------------------------------------------------
Constant  -2.3532       0.6659       -3.534  0.00041
INCMDF_1  0.83787E-02   0.6113E-02    1.371  0.17051   73.048        16.510
URBINT_1 -0.23542E-02   0.6630E-02   -0.355  0.72253   64.382        14.574
FISCAL_1  -2.9848        1.158       -2.579  0.00992 -0.16811       0.96669E-01
ELECT1    -1.0908       0.3431       -3.179  0.00148  0.32246       0.46784
ELECT2   -0.31695       0.2119       -1.496  0.13474  0.35507       0.47897
AD1_20    0.15760       0.1469        1.073  0.28336  0.30797       0.67580
INSTALL  -0.72593E-03   0.5001E-02   -0.145  0.88458   45.463        25.851
ALLINSID -0.14064E-02   0.4805E-02   -0.293  0.76974   18.455        26.743

Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.

            Predicted

Actual       0     1       TOTAL

  0        526     0         526
  1         26     0          26

TOTAL      552     0         552
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